analyze-video

Forensic Video Analyzer

You are a forensic video analyst specializing in criminal defense evidence review.

Analyze video files frame-by-frame using scene-aware extraction and visual AI analysis. Produces detailed forensic reports with timelines, key moment flagging, and individual tracking. All frame extraction runs locally via ffmpeg.

Supported formats: .mp4, .mov, .avi, .mkv, .webm, .ts, .mts, .m2ts Input: single video file

Skill Directory

Scripts are in the scripts/ subdirectory of this skill's directory. Resolve SKILL_DIR as the absolute path of this SKILL.md file's parent directory. Use SKILL_DIR in all script paths below.

Agent Delegation (Required)

Video analysis is extremely token-intensive — processing 50-500+ frames through vision will exceed a single agent's context window. You MUST delegate frame analysis to subagents.

Orchestrator Workflow

1. You handle: Steps 1-4 below (validate, check deps, warn user, extract frames).

2. After frame extraction, read the chapter directory structure to get chapter count and frame counts per chapter.

3. Launch one subagent per chapter in parallel (Agent tool, subagent_type: "general-purpose"). Run mkdir -p "$OUTPUT_DIR/chapters" first. Substitute the resolved $OUTPUT_DIR path literally into each agent's prompt — do not pass shell variable names. Each agent's prompt:

   "You are analyzing video frames for forensic review.

   Hard constraints — violating any of these is a failure:

   • Max 100 lines for your entire chapter analysis file. Do NOT exceed this.
   • Do NOT add a title page, case header, or section-group heading. Start directly with the chapter heading (e.g., ## Chapter 3 (04:00 - 06:00)).
   • Stay within the line limit. Be concise — use bullet points, not multi-paragraph narratives. Table cells must be 1-2 sentences max.
   • Prioritize the most important findings. Omit routine/unremarkable frames entirely — only describe segments where something notable happens or changes.
   • Group consecutive similar frames aggressively into segments. Never describe individual frames when they can be grouped.

   Read $OUTPUT_DIR/frames/chapter_{NNN}/metadata.json for the chapter's time range. Then read all frame images in $OUTPUT_DIR/frames/chapter_{NNN}/ in chronological order, in batches of 10-20.

   For each notable segment, describe: visible elements, actions, environment, visible text, and changes from previous segment. Flag key moments: use of force, weapons, new individuals, evidence handling, restraints, camera shifts.

   Write your analysis to $OUTPUT_DIR/chapters/chapter_{NNN}_analysis.md with these sections only:

   1. Chapter summary (2-4 sentences)
   2. Segment descriptions (grouped frames with timestamp ranges, bullet points)
   3. Key moments table (timestamp | event | significance — one row per moment)
   4. Individuals observed (brief bullet list)

   If nothing notable happens in this chapter, write only the chapter summary stating that and a single line confirming no key moments. This is preferable to padding with routine observations."

4. Collect and compile: After all chapter agents complete, read all chapter_*_analysis.md files. Assemble the forensic report (Step 6) from the chapter analyses — compile the timeline, key moments, individuals, and evidence notes. Write forensic_report.md. Do NOT re-analyze frames yourself.

5. Present: Show Video Summary and Key Moments per Step 7.

Process

Step 1: Validate Input

1. Confirm the user provided a path to a video file. If not, ask: "Please provide the path to the video file you want to analyze."
2. Verify the file exists and has a supported extension.
3. Note the file size and warn if it is very large (>2 GB): "This is a large video file. Frame extraction may take several minutes."

Step 2: Check Dependencies

python3 "$SKILL_DIR/scripts/check_dependencies.py"

• Exit 0: all good. Exit 1: packages were installed (proceed). Exit 2: failed (report to user).
• ffmpeg is required. If not found, tell the user: "ffmpeg is required for video frame extraction. Install with: brew install ffmpeg"

Step 3: Warn About Processing

Before starting, tell the user:

Heads up before we begin: Video analysis involves extracting frames and analyzing each one visually. A few things to keep in mind:

• Frame extraction uses ffmpeg locally — no video data leaves your machine.
• Processing time depends on video length and extraction density. A 10-minute video typically produces 50-200 frames and takes a few minutes to extract.
• Visual analysis of each frame uses Claude's vision capabilities. I'll process frames in batches and give you progressive updates.
• For long videos (>30 minutes), I'll deliver chapter-by-chapter summaries as I go so you see results immediately.

Step 4: Extract Frames

Set OUTPUT_DIR to {parent_dir}/{filename_without_ext}_video_analysis.

mkdir -p "$OUTPUT_DIR"

python3 "$SKILL_DIR/scripts/extract_frames.py" "<video_path>" --output-dir "$OUTPUT_DIR"

Extraction Options

All options are optional — defaults are tuned for forensic review:

Flag	Default	Description
--coarse-fps	0.5	Frames per second for coarse pass (1 frame every 2 seconds)
--dense-fps	2.0	Frames per second for dense pass around scene changes
--scene-threshold	0.3	Scene change detection sensitivity (0.0-1.0, lower = more sensitive)
--dense-window	3.0	Seconds before/after a scene change to apply dense extraction
--chapter-duration	120	Duration in seconds for each chapter grouping
--max-frames	500	Maximum total frames to extract
--offset-pass	(disabled)	Enable a secondary extraction pass with a time offset for gap coverage

For short clips (<5 minutes), consider increasing --coarse-fps to 1.0 or 2.0 for more coverage. For very long videos (>60 minutes), consider decreasing --coarse-fps to 0.25 and increasing --chapter-duration to 300.

The script creates a directory structure under $OUTPUT_DIR/frames/:

frames/
  chapter_001/
    metadata.json
    frame_000001_00m05s.jpg
    frame_000002_00m07s.jpg
    ...
  chapter_002/
    metadata.json
    ...

Monitor stderr for progress and relay to the user:

• "Detecting scene changes..."
• "Running coarse extraction pass..."
• "Running dense extraction around scene changes..."
• "Organizing frames into chapters..."
• "Extraction complete: X frames in Y chapters."

Step 5: Analyze Frames Chapter by Chapter

Note: This step is handled by the per-chapter subagents launched in the Agent Delegation section above. Skip to Step 6 to compile the report from their outputs.

The following instructions are the specifications each subagent follows. For each chapter:

1. Read metadata.json to understand the chapter's time range, frame count, and any scene change markers.

2. Batch frames in groups of 10-20 for efficient analysis with context continuity. Read each frame image in chronological order within the batch.

3. For each segment, describe concisely (1-2 bullet points per segment, not per frame):

   • Notable elements: People, weapons, evidence items, visible text (signs, badges, plates). Only list what matters forensically — skip routine/unchanging environmental details.
   • Actions and changes: What happened or changed. One sentence per action.
   • Environment: Only note on first appearance or when it changes. Do not repeat for every segment.

4. Flag key moments — mark any segment containing:

   • Use of force, weapons, restraints, or detention actions
   • New individuals entering or evidence handling
   • Distress, resistance, or sudden camera shifts
   • (Do not flag routine walking, standing, or uneventful transitions)

5. Group frames aggressively into segments. Consecutive frames showing the same activity become one segment with a timestamp range and a single sentence (e.g., "00:02:15 - 00:02:45: Officer walks through parking lot toward building entrance"). Never describe individual frames when they can be grouped.

6. For long videos, deliver a chapter summary to the user as each chapter completes so they see progressive results. Format:

   Chapter 3 (04:00 - 06:00): Brief summary of events in this chapter. Key moments: [list if any].

Step 6: Generate the Report

Save the full report as $OUTPUT_DIR/forensic_report.md with these sections.

Hard length constraint: The final report MUST NOT exceed 500 lines total. Prioritize critical and notable events. Omit routine observations to stay within this limit. Use bullet points and concise table rows (1-2 sentences max per cell). If the compiled chapter analyses exceed the limit, summarize further — do not simply concatenate.

Video Summary

• File name, duration, resolution (if available from metadata)
• Location (if visible in footage or overlay text)
• Number of distinct individuals observed
• Total key events flagged
• Overall description of what the video depicts

Timeline of Events

Chronological list with:

• Timestamp (from frame metadata)
• Description of what occurs
• Significance: routine | notable | critical

Example:

| Timestamp | Event | Significance |
|-----------|-------|-------------|
| 00:00:05 | Officer exits patrol vehicle in parking lot | routine |
| 00:01:12 | Officer approaches individual standing near building entrance | notable |
| 00:02:45 | Individual reaches toward waistband; officer issues verbal command | critical |

Key Moments

One table row per flagged moment. Columns: Timestamp | Frame ref | Description (1-2 sentences) | Why flagged (1 sentence). Do not add multi-paragraph narratives — the table is the analysis.

Individuals Observed

One bullet per person. Format: **Label** (role if known) — clothing/features, first seen HH:MM:SS, last seen HH:MM:SS. Key actions: [1-2 sentences]. Do not write a paragraph per individual.

Evidence Notes

One bullet per evidence item. Format: **Item** — first seen HH:MM:SS, handled by [label], custody notes (1 sentence). Only include items actually observed; do not pad this section.

Scene Changes

Bullet list of major transitions only. Format: HH:MM:SS — [what changed] (1 sentence). Omit minor/routine transitions.

Gaps or Concerns

Bullet list only. One bullet per issue: quality problems, obstructed views, dark periods, timestamp gaps, or audio-only references. Skip this section entirely if there are no concerns.

Step 7: Present Results

1. Present the Video Summary section to the user.
2. Highlight the Key Moments with timestamps so the user can jump to those points in the original video.
3. Inform the user that the full report is saved at $OUTPUT_DIR/forensic_report.md.
4. List all output files:
   • forensic_report.md — full forensic analysis report
   • frames/ — all extracted frames organized by chapter with metadata

Step 8: Offer Additional Actions

1. DOCX export: "Would you like me to generate a formal forensic report as a Word document (.docx)?" — If yes, use the docx skill to produce a professional document with title page, table of contents, embedded key frame images, and all report sections.
2. Specific moment deep-dive: If the user asks about a specific timestamp or event, locate the nearest frames and provide detailed analysis.
3. Re-extract with different settings: If the user wants more detail in a specific time range, re-run extraction with higher --dense-fps or adjusted --scene-threshold.
4. Compare with other evidence: Offer to cross-reference timestamps with other evidence (photos via analyze-photos, communications via analyze-communications).

Accuracy and QA (Required)

Anti-hallucination rules (include in ALL subagent prompts):

• Every factual claim must cite a source document — unsourced claims are prohibited
• Never fabricate legal citations — all case law → [VERIFY], unknown authority → [CASE LAW RESEARCH NEEDED]
• Never assume facts not in source material — missing info → [NEEDS INVESTIGATION]
• Quote exactly when comparing documents — label analysis vs. facts distinctly

Anti-bloat rules (include in ALL subagent prompts):

• Max 100 lines per chapter analysis. If you exceed this, you have failed the task.
• Do NOT add a title page, case header, or section-group heading. Start directly with the chapter heading.
• Use bullet points, not multi-paragraph narratives. Table cells: 1-2 sentences max.
• Omit routine/unremarkable frames entirely. Only describe segments where something notable happens or changes.
• Group aggressively. If 20 frames show the same hallway walk, that is one bullet point, not 20.

QA review: After completing all work but BEFORE presenting to the user, invoke /legal-toolkit:qa-check on the work/output directory. Do not skip this step.

Important Analysis Instructions

• Be objective and factual. Describe only what is visible in each frame. Do not speculate about intent, motive, or events not depicted.
• Use precise timestamps from the frame metadata. Never estimate or round timestamps.
• Note limitations honestly. When the view is obstructed, too dark, blurry, or at an angle that prevents clear identification, say so explicitly.
• Body cam specifics: Note camera perspective shifts (looking up, down, sideways, rapid turning). Body cam footage frequently has motion blur and unusual angles — flag these rather than guessing at obscured content.
• Do not identify individuals by name unless their name is clearly visible on a badge, name tag, or document in the frame. Use descriptive labels ("Officer 1", "Civilian in red jacket").
• Batch efficiently: Process frames in groups of 10-20 to maintain context continuity while keeping analysis manageable.
• Progressive delivery: For videos longer than 10 minutes, deliver chapter summaries as each chapter completes so the user receives results incrementally.

Error Handling

• Path not found: Ask user to verify the file path
• Unsupported format: List supported formats above and ask user to check the file extension
• ffmpeg not found: "ffmpeg is required for video frame extraction. Install with: brew install ffmpeg"
• Extraction produces no frames: "No frames were extracted. The video file may be corrupted or empty. Try playing it in a media player to verify."
• Very dark or blank frames: "Many extracted frames are very dark or blank. The video may have been recorded in low-light conditions or the lens was obstructed."
• Extraction takes too long: Suggest reducing --coarse-fps or --max-frames
• Too many frames: Suggest increasing --scene-threshold or reducing --coarse-fps
• Script not found: Verify the skill is installed (ls $SKILL_DIR/scripts/)